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Purpose: The purpose of this study was to evaluate the efficacy of auditory training (AT) in patients with unilateral hearing loss (UHL) using hearing aids (HAs), comparing traditional methods with a new approach involving a wireless remote microphone. Methods: The study included 96 participants, divided into two groups, with ages ranging from 42 to 64 years, comprising both male and female subjects. A clinical trial including consecutive moderate UHL patients was performed at our institution. For the study group, a Roger Pen was used during AT with patients inside a sound-attenuating cabin. Controls followed conventional sessions. Professional speech and language pathologists performed the rehabilitation. Audiological outcomes were measured, including word recognition at signal-to-noise ratios (SNRs) of 0 dB, +5 dB, and +10 dB, to determine the effectiveness of the training. Measurements also included the Speech, Spatial, and Qualities of Hearing Scale to assess perceived auditory abilities. Results: A total of 46 and 50 UHL patients were randomly included in the study and control groups, respectively. No differences were found in terms of sex, age, presence of tinnitus, duration of hearing loss, pure tone average, and speech-in-noise perception without an HA. Following HA fitting and AT, a notable enhancement in the ability to identify speech in noisy environments was observed in the study group. This improvement was significant at SNRs of +5 and +10. When comparing the ability to identify speech in noise using HAs across both groups, it was observed that hearing capabilities post-wireless AT showed a significant improvement at an SNR of +5. Only the study group had a significant improvement in the total Speech, Spatial, and Qualities of Hearing Scale score after the training. Conclusions: In our group of UHL patients, we found significantly better speech-in-noise perception when HA fitting was followed by wireless AT. Wireless AT may facilitate usage of HAs, leading to binaural hearing in UHL patients. Our findings suggest that future interventions might benefit from incorporating wireless technology in AT programs.

Ultra-wideband radio signals have been the subject of study for several decades. They are used to solve problems of communications and ranging. Measuring the strength (power) of a radio signal is a technically simple way to estimate the distance between the emitter and the receiver of the signal. However, the conditions of signal propagation have a significant impact on the power of the received signal. This work is relevant because chaotic radio pulses are a relatively new type of carrier in wireless technologies, and actual knowledge about the change in signal power in different types of premises is relatively small, so such a study is necessary. In this paper, we study the variation in signal power with distance for chaotic ultra-wideband radio pulses under various propagation conditions. Using experimental measurements in several outdoor (field, roadside) and indoor (corridors, conference room, office) environments, we investigate the effect of propagation conditions on ultra-wideband chaotic radio signals and determine the limits within which the dependence of the calculated power on distance can be approximated by a power law. For this purpose, the results of experimental measurements of the received signal power (a total of about 17.5 M values) were accumulated and analyzed. The accuracy of distance measurement that can be achieved in different conditions is compared and analyzed. It was found that for a 9.5 dBm signal, the range of distances at which the average accuracy is only 15–50 cm when using a power law is 5–7 m indoors and 10–15 m outdoors.

One aspect of reproducibility in preclinical research that is frequently overlooked is the physical condition in which physiological, pharmacological, or behavioural recordings are conducted. In this study, the physical conditions of mice were altered through the attachments of wireless electrophysiological recording devices (Neural Activity Tracker-1, NAT-1). NAT-1 devices are miniaturised multichannel devices with onboard memory for direct high-resolution recording of brain activity for >48 h. Such devices may limit the mobility of animals and affect their behavioural performance due to the added weight (total weight of approximately 3.4 g). The mice were additionally treated with saline (control), N-methyl-D-aspartate (NMDA) receptor antagonist MK801 (0.85 mg/kg), or the muscarinic acetylcholine receptor blocker scopolamine (0.65 mg/kg) to allow exploration of the effect of NAT-1 attachments in pharmacologically treated mice. We found only minimal differences in behavioural outcomes with NAT-1 attachments in standard parameters of locomotor activity widely reported for the open field test between the drug treatments. Hypoactivity was globally observed as a consistent outcome in the MK801-treated mice and hyperactivity in scopolamine groups regardless of NAT-1 attachments. These data collectively confirm the reproducibility for combined behavioural, pharmacological, and physiological endpoints even in the presence of lightweight wireless data loggers. The NAT-1 therefore constitutes a pertinent tool for investigating brain activity in, e.g., drug discovery and models of neuropsychiatric and/or neurodegenerative diseases with minimal effects on pharmacological and behavioural outcomes.

This comprehensive article explores the massive MIMO (M-MIMO) design and its associated concepts, focusing on the seamless integration requirements for Beyond 5G (B5G) and 6G networks. Addressing critical aspects such as RF chain reduction, pilot contamination, cell-free MIMO, and security considerations, this article delves into the intricacies of M-MIMO in the evolving landscape of B5G. Moreover, the emerging MIMO concepts in this article include AI-enabled M-MIMO three-dimensional beamforming, reconfigurable intelligent surfaces, visible light communication, and THz spectrum utilization. This review highlights the challenges and open research issues, including Narrow Aperture Antenna Nodes, Plasmonic Antenna Arrays, Integrated Sensing with M-MIMO, and the application of federated learning in M-MIMO systems. By examining these cutting-edge developments, this article aims to advance knowledge in the field and inspire future research directions in the exciting realm of B5G and 6G networks.

Residential consumption represents one of the most important percentages of total electricity consumption. A considerable number of household appliances consume energy even when they are not in operation, i.e., they are in the so-called standby state, thus producing additional costs, which become significant over time. In this context, one method to solve this problem is to develop a smart system capable of severing the power connection to devices in standby mode, thereby conserving energy and reducing the energy costs. The first step in the design of this system consists of the identification and accurate measurement of the standby state, which was carried out for three of the most common household appliances. Then, by using an ESP32 microcontroller, a system was designed to manage the operation of a relay module based on the current consumption of the connected equipment. Control over the system was achieved through a web application that works across all devices equipped with a web browser, offering functionalities to adjust current value time delays and to manually switch the system on or off. Finally, the deployment of this system across the three appliances studied led to a reduction in the energy consumption in standby mode of 26.68 kWh per month.

Wearable devices, such as headsets and activity trackers, rely heavily on the Bluetooth and/or the Bluetooth Low Energy wireless communication standard to exchange data with smartphones or other peripherals. Since these devices collect personal health and activity data, ensuring the privacy and security of the transmitted data is crucial. Therefore, we present a dataset that captures complete Bluetooth communications—including advertising, connection, data exchange, and disconnection—in an RF isolated environment using software-defined radio. We were able to successfully decode the captured Bluetooth packets using existing tools. This dataset provides researchers with the ability to fully analyze Bluetooth traffic and gain insight into communication patterns and potential security vulnerabilities.

The use of wireless sensors for real-time sensing of substrate water status and electrical conductivity could be an effective tool for precision irrigation management in soilless cultivation. In this research, the effects of timer-based (TB) compared to smart sensor-based irrigation (SB) were investigated. The highest consumption of fertilizers and water were recorded in TB, with nutrient solution and total applied water savings of 38% and 26%, respectively, in SB. The highest yield was obtained in SB treatment, with a total and marketable yield decrease of 7% in TB, with no differences in terms of the total soluble solids content, dry matter, firmness, juice pH and titratable acidity of the strawberry fruits. The higher yield, combined with water and nutrient saving in SB, allowed water use efficiency (fresh weight of marketable fruits per liter of total water applied) to be increased by 46% and nutrient productivity (fresh weight of marketable product per gram of nutrient supplied via nutrient solution) by 74%. The study confirms that sensor-based, compared to empiric fertigation management, ameliorates the sustainability of open, free-drain, soilless cultivation of strawberry, leading to better resource use without compromising crop performance and fruit quality.

Aim: The aim of our study was to investigate skin conditions when wearing and removing a novel wireless non-adhesive cardiorespiratory monitoring device for neonates (Bambi-Belt) compared to standard adhesive electrodes. Study Design: This was a prospective study including preterm neonates requiring cardiorespiratory monitoring. Besides standard electrodes, the infants wore a Bambi Belt for 10 consecutive days. Their skin conditions were assessed using Trans Epidermal Water Loss (TEWL) and the Neonatal Skin Condition Score (NSCS) after daily belt and standard electrode removal. The ∆TEWL was calculated as the difference between the TEWL at the device’s location (Bambi-Belt/standard electrode) and the adjacent control skin location, with a higher ∆TEWL indicating skin damage. Results: A total of 15 infants (gestational age (GA): 24.1–35.6 wk) were analyzed. The ΔTEWL significantly increased directly after electrode removal (10.95 ± 9.98 g/m2/h) compared to belt removal (5.18 ± 6.71 g/m2/h; F: 8.73, p = 0.004) and after the washout period (3.72 ± 5.46 g/m2/h vs. 1.86 ± 3.35 g/m2/h; F: 2.84, p = 0.09), although the latter did not reach statistical significance. The TEWL was not influenced by prolonged belt wearing. No significant differences in the NSCS score were found between the belt and electrode (OR: 0.69, 95% CI [0.17, 2.88], p = 0.6). Conclusion: A new wireless non-adhesive device for neonatal cardiorespiratory monitoring was well tolerated in preterm infants and may be less damaging during prolonged wearing.

In this study, we developed a novel wireless, passive pressure-sensing method functional at cryogenic temperatures (−196 °C). The currently used pressure sensors are inconvenient and complicated in cryogenic environments for their weak low-temperature tolerances and long wires for power supply and data transmission. We propose a novel pressure-sensing method for cryogenic applications by only using low-temperature-tolerant passive devices. By innovatively integrating a magnetoresistor (MR) on a backscattering antenna, the pressure inside a cryogenic environment is transferred to a wirelessly obtainable return loss. Wireless passive measurement is thus achieved using a backscattering method. In the measurement, the pressure causes a relative displacement between the MR and a magnet. The MR’s resistance changes with the varied magnetic field, thus modulating the antenna’s return loss. The experimental results indicate that our fabricated sensor successfully identified different pressures, with high sensitivities of 4.3 dB/MPa at room temperature (24 °C) and 1.3 dB/MPa at cryogenic temperature (−196 °C). Additionally, our method allows for simultaneous wireless readings of multi sensors via a single reading device by separating the frequency band of each sensor. Our method performs low-cost, simple, robust, passive, and wireless pressure measurement at −196 °C; thus, it is desirable for cryogenic applications.

Highly sensitive surface acoustic wave (SAW) sensors have recently been recognized as a promising tool for various industrial and medical applications. However, existing SAW sensors generally suffer from a complex design, large size, and poor robustness. In this paper, we develop a simple and stable delay line ultra-high frequency (UHF) SAW sensor for highly sensitive detection of temperature. A Z-shaped delay line is specially designed on the piezoelectric substrate to improve the sensitivity and reduce the substrate size. Herein, the optimum design parameters of extremely short-pitch interdigital transducers (IDTs) are given by numerical simulations. The extremely short pitch gives the SAW sensor ultra-high operating frequency and consequently ultra-high sensitivity. Several experiments are conducted to demonstrate that the sensitivity of the Z-shaped SAW delay line sensor can reach up to 116.685°/°C for temperature detection. The results show that the sensor is an attractive alternative to current SAW sensing platforms in many applications.

In this study, a new wireless electronic circuitry to analyze weight distribution was designed and incorporated into a chair to gather data related to common human postures (sitting and standing up). These common actions have a significant impact on various motor capabilities, including gait parameters, fall risk, and information on sarcopenia. The quality of these actions lacks an absolute measurement, and currently, there is no qualitative and objective metric for it. To address this, the designed analyzer introduces variables like Smoothness and Percussion to provide more information and objectify measurements in the assessment of stand-up/sit-down actions. Both the analyzer and the proposed variables offer additional information that can objectify assessments depending on the clinical eye of the physicians.

Despite the evolution of hearing aids and cochlear implants, noisy environments are reportedly still an important hurdle for persons with hearing loss, especially in the process of speech recognition. The development of pre-processing algorithms and the pairing with a wireless device can bring relief to this situation, but it is still under scrutiny whether one or the other is more effective. The purpose of this study was to compare the benefits of speech recognition in a noisy environment by recipients of cochlear implants when using the pre-processing automatic algorithms or when using a wireless microphone. Twenty-nine participants were selected, aged 14 to 83, suffering from sensorineural hearing loss and recipients of cochlear implants for at least 6 months. The proprietary Cochlear Limited SCAN technology uses pre-processing algorithms to attenuate various noises; the wireless device MiniMic2 uses a 2.4 GHz connection to facilitate communications between the recipient and the signal source. Participants were asked to repeat 20 sentences randomly generated by the adaptive Italian Matrix Sentence Test, first while using the SCAN technology and then with the wireless MiniMic2. Both signal and noise were administered through a single loudspeaker set 1 m away from the subject. Significantly better results in speech recognition of noise were achieved with the wireless MiniMic2 when compared to the SCAN technology.

Electroencephalography (EEG) is the most common method to access brain information. Techniques to monitor and extract brain signal characteristics in farm animals are not as developed as those for humans and laboratory animals. The objective of this study was to develop a noninvasive method for monitoring brain signals in cattle, allowing the animals to move freely, and to characterize these signals. Brain signals from six Holstein heifers that could move freely in a paddock compartment were acquired. The control group consisted of the same number of bovines, contained in a climatic chamber (restrained group). In the second step, the signals were characterized by Power Spectral Density, Short-Time Fourier Transform, and Lempel–Ziv complexity. The preliminary results revealed an optimal electrode position, referred to as POS2, which is located at the center of the frontal region of the animal’s head. This positioning allowed for attaching the electrodes to the front of the bovine’s head, resulting in the acquisition of longer artifact-free signal sections. The signals showed typical EEG frequency bands, like the bands found in humans. The Lempel–Ziv complexity values indicated that the bovine brain signals contained random and chaotic components. As expected, the signals acquired from the retained bovine group displayed sections with a larger number of artifacts due to the hot 32 degree C temperature in the climatic chamber. We present a method that helps to monitor and extract brain signal features in unrestrained bovines. The method could be applied to investigate changes in brain electrical activity during animal farming, to monitor brain pathologies, and to other situations related to animal behavior.

Multiconnectivity allows user equipment/devices to connect to multiple radio access technologies simultaneously, including 5G, 4G (LTE), and WiFi. It is a necessity in meeting the increasing demand for mobile network services for the 5G and beyond wireless networks, while ensuring that mobile operators can still reap the benefits of their present investments. Multipath TCP (MPTCP) has been introduced to allow uninterrupted reliable data transmission over multiconnectivity links. However, energy consumption is a significant issue for multihomed wireless devices since most of them are battery-powered. This paper employs software-defined networking (SDN) and deep neural networks (DNNs) to manage the energy consumption of devices with multiconnectivity running MPTCP. The proposed method involves two lightweight algorithms implemented on an SDN controller, using a real hardware testbed of dual-homed wireless nodes connected to WiFi and cellular networks. The first algorithm determines whether a node should connect to a specific network or both networks. The second algorithm improves the selection made by the first by using a DNN trained on different scenarios, such as various network sizes and MPTCP congestion control algorithms. The results of our extensive experimentation show that this approach effectively reduces energy consumption while providing better network throughput performance compared to using single-path TCP or MPTCP Cubic or BALIA for all nodes.

The rise in internet users has brought with it the impending threat of cybercrime as the Internet of Things (IoT) increases and the introduction of 5G technologies continues to transform our digital world. It is now essential to protect communication networks from illegal intrusions to guarantee data integrity and user privacy. In this situation, machine learning techniques used in data mining have proven to be effective tools for constructing intrusion detection systems (IDS) and improving their precision. We use the well-known AWID3 dataset, a comprehensive collection of wireless network traffic, to investigate the effectiveness of machine learning in enhancing network security. Our work primarily concentrates on Krack and Kr00k attacks, which target the most recent and dangerous flaws in IEEE 802.11 protocols. Through diligent implementation, we were able to successfully identify these threats using an IDS model that is based on machine learning. Notably, the resilience of our method was demonstrated by our ensemble classifier’s astounding 99% success rate in detecting the Krack attack. The effectiveness of our suggested remedy was further demonstrated by the high accuracy rate of 96.7% displayed by our neural network-based model in recognizing instances of the Kr00k attack. Our research shows the potential for considerably boosting network security in the face of new threats by leveraging the capabilities of machine learning and a diversified dataset. Our findings open the door for stronger, more proactive security measures to protect IEEE. 802.11 networks’ integrity, resulting in a safer online environment for all users.

Fiber reinforced plastics (FRP) offer huge potentials for energy efficient applications. Special care must be taken during both FRP fabrication and usage to ensure intended material properties and behavior. This paper presents a novel approach for the monitoring of the strain and temperature of glass fibre reinforced polymer (GFRP) materials in the context of both production process monitoring and structural health monitoring (SHM) applications. The sensor is designed to be integrated into GFRPs during the production process, and the sensor concept includes possibilities of automated placement during textile layup. To minimize sensor impact on GFRP integrity and to simplify vacuum setup and part handling, the sensor operates without the need for either wires or a battery. In the first sections of this work, sensor concept, design and prototype fabrication are presented. Subsequently, it is shown how the sensors can be used for flow front monitoring and cure estimation during GFRP production by measuring local resin temperature. The resulting specimens are then characterized regarding strain measurement capabilities, mechanical influence on the host component and overall system limitations. Average strain sensor accuracy is found to be ≤0.06 mm/m, while a maximum operation temperature of 126.9 °C and a maximum reading distance of 38 mm are measured. Based on a limited number of bending tests, no negative influence of sensor presence on breaking strength could be found. Possible applications include structural components, e.g., wind turbine blades or boat hulls.

This article outlines the implementation and use of a large wireless instrumentation solution to collect data over a long time period of a few years for three collective residential buildings. The sensor network consists of a variety of 179 sensors deployed in building common areas and in apartments to monitor energy consumption, indoor environmental quality, and local meteorological conditions. The collected data are used and analyzed to assess the building performance in terms of energy consumption and indoor environmental quality following major renovation operations on the buildings. Observations from the collected data show energy consumption of the renovated buildings in agreement with expected energy savings calculated by an engineering office, many different occupancy patterns mainly related to the professional situation of the households, and seasonal variation in window opening rates. The monitoring was also able to detect some deficiencies in the energy management. Indeed, the data reveal the absence of time-of-day-dependent heating load control and higher than expected indoor temperatures because of a lack of occupant awareness on energy savings, thermal comfort, and the new technologies installed during the renovation such as thermostatic valves on the heaters. Lastly, we also provide feedback on the performed sensor network from the experiment design and choice of measured quantities to data communication, through the sensors’ technological choices, implementation, calibration, and maintenance.

In orthodontics, understanding the pressure of oral soft tissues on teeth is important to elucidate the cause and establish treatment methods. We developed a small wireless mouthguard (MG)-type device that continuously and unrestrainedly measures pressure, which had previously been unachieved, and evaluated its feasibility in human subjects. First, the optimal device components were considered. Next, the devices were compared with wired-type systems. Subsequently, the devices were fabricated for human testing to measure tongue pressure during swallowing. The highest sensitivity (51–510 g/cm2) with minimum error (CV < 5%) was obtained using an MG device with polyethylene terephthalate glycol and ethylene vinyl acetate for the lower and upper layers, respectively, and with a 4 mm PMMA plate. A high correlation coefficient (0.969) was observed between the wired and wireless devices. In the measurements of tongue pressure on teeth during swallowing, 132.14 ± 21.37 g/cm2 for normal and 201.17 ± 38.12 g/cm2 for simulated tongue thrust were found to be significantly different using a t-test (n = 50, p = 6.2 × 10−19), which is consistent with the results of a previous study. This device can contribute to assessing tongue thrusting habits. In the future, this device is expected to measure changes in the pressure exerted on teeth during daily life.

Wearable wireless electrocardiographic (ECG) monitoring is well-proven for arrythmia detection, but ischemia detection accuracy is not well-described. We aimed to assess the agreement of ST-segment deviation from single- versus 12-lead ECG and their accuracy for the detection of reversible ischemia. Bias and limits of agreement (LoA) were calculated between maximum deviations in ST segments from single- and 12-lead ECG during 82Rb PET-myocardial cardiac stress scintigraphy. Sensitivity and specificity for reversible anterior-lateral myocardial ischemia detection were assessed for both ECG methods, using perfusion imaging results as a reference. Out of 110 patients included, 93 were analyzed. The maximum difference between single- and 12-lead ECG was seen in II (−0.019 mV). The widest LoA was seen in V5, with an upper LoA of 0.145 mV (0.118 to 0.172) and a lower LoA of −0.155 mV (−0.182 to −0.128). Ischemia was seen in 24 patients. Single-lead and 12-lead ECG both had poor accuracy for the detection of reversible anterolateral ischemia during the test: single-lead ECG had a sensitivity of 8.3% (1.0–27.0%) and specificity of 89.9% (80.2–95.8%), and 12-lead ECG a sensitivity of 12.5% (3.0–34.4%) and a specificity of 91.3% (82.0–96.7%). In conclusion, agreement was within predefined acceptable criteria for ST deviations, and both methods had high specificity but poor sensitivity for the detection of anterolateral reversible ischemia. Additional studies must confirm these results and their clinical relevance, especially in the light of the poor sensitivity for detecting reversible anterolateral cardiac ischemia.

Magnetoelastic sensors, which undergo mechanical resonance when interrogated with magnetic fields, can be functionalized to measure various physical quantities and chemical/biological analytes by tracking their resonance behaviors. The unique wireless and functionalizable nature of these sensors makes them good candidates for biological sensing applications, from the detection of specific bacteria to tracking force loading inside the human body. In this study, we evaluate the viability of magnetoelastic sensors based on a commercially available magnetoelastic material (Metglas 2826 MB) for wirelessly monitoring the attachment and growth of human mesenchymal stromal cells (hMSCs) in 2D in vitro cell culture. The results indicate that the changes in sensor resonance are linearly correlated with cell quantity. Experiments using a custom-built monitoring system also demonstrated the ability of this technology to collect temporal profiles of cell growth, which could elucidate key stages of cell proliferation based on acute features in the profile. Additionally, there was no observed change in the morphology of cells after they were subjected to magnetic and mechanical stimuli from the monitoring system, indicating that this method for tracking cell growth may have minimal impact on cell quality and potency.

The survival rate for sudden cardiac arrest (SCA) is low, and patients with long-term risks of SCA are not adequately alerted. Understanding SCA’s characteristics will be key to developing preventive strategies. Many lives could be saved if SCA’s early onset could be detected or predicted. Monitoring heart signals continuously is essential for diagnosing sporadic cardiac dysfunction. An electrocardiogram (ECG) can be used to continuously monitor heart function without having to go to the hospital. A zeolite-based dry electrode can provide safe on-skin ECG acquisition while the subject is out-of-hospital and facilitate long-term monitoring. To the ECG signal, a low-power 1 μW read-out circuit was designed and implemented in our prior work. However, having long-term ECG monitoring outside the hospital, i.e., high battery life, and low power consumption while transmission and reception of ECG signal are crucial. This paper proposes a prototype with a 10-bit resolution ADC and nRF24L01 transceivers placed 5 m apart. The system uses the 2.4 GHz worldwide ISM frequency band with GFSK modulation to wirelessly transmit digitized ECG bits at 250 kbps data rate to a physician’s computer (or similar) for continuous monitoring of ECG signals; the power consumption is only 11.2 mW and 4.62 mW during transmission and reception, respectively, with a low bit error rate of ≤0.1%. Additionally, a subject-wise cross-validated, three-fold, optimized convolutional neural network (CNN) model using the Physionet-SCA dataset was implemented on NVIDIA Jetson to identify the irregular heartbeats yielding an accuracy of 89% with a run time of 5.31 s. Normal beat classification has an F1 score of 0.94 and a ROC score of 0.886. Thus, this paper integrates the ECG acquisition and processing unit with low-power wireless transmission and CNN model to detect irregular heartbeats.

A wireless multi-layered sensor that allows electromyography (EMG), mechanomyography (MMG) and near-infrared spectroscopy (NIRS) measurements to be carried out simultaneously is presented. The multi-layered sensor comprises a thin silver electrode, transparent piezo-film and photosensor. EMG and MMG measurements are performed using the electrode and piezo-film, respectively. NIRS measurements are performed using the photosensor. Muscular activity is then analyzed in detail using the three types of data obtained. In experiments, the EMG, MMG and NIRS signals were measured for isometric ramp contraction at the forearm and cycling exercise of the lateral vastus muscle with stepped increments of the load using the layered sensor. The results showed that it was possible to perform simultaneous EMG, MMG and NIRS measurements at a local position using the proposed sensor. It is suggested that the proposed sensor has the potential to evaluate muscular activity during exercise, although the detection of the anaerobic threshold has not been clearly addressed.

The Internet of Health Things (IoHT) has emerged as an attractive networking paradigm in wireless communications, integrated devices and embedded system technologies. In the IoHT, real-time health data are collected through smart healthcare sensors and, in recent years, the IoHT has started to have an important role in the Internet of Things technology. Although the IoHT provides comfort in health monitoring, it also imposes security challenges in maintaining patient data confidentiality and privacy. To overcome such security issues, in this paper, a novel blockchain-based privacy-preserving authentication scheme is proposed as an approach for achieving efficient authentication of the patient without the involvement of a trusted entity. Moreover, a secure handover authentication mechanism that ensures avoiding the patient re-authentication in multi-doctor communication scenarios and revoking the possible malicious misbehavior of medical professionals in the IoHT communication with the patient is developed. The performance of the proposed authentication and handover scheme is analyzed concerning the existing state-of-the-art authentication schemes. The results of the performance analyses reveal that the proposed authentication scheme is resistant to different types of security attacks. Moreover, the results of analyses show that the proposed authentication scheme outperforms similar state-of-the-art authentication schemes in terms of having lower computational, communication and storage costs. Therefore, the novel authentication and handover scheme has proven practical applicability and represents a valuable contribution to improving the security of communication in IoHT networks.

This paper proposes a multiple-lens receiver scheme to increase the misalignment tolerance of an underwater optical wireless communications link between an autonomous underwater vehicle (AUV) and a sensor plane. An accurate model of photon propagation based on the Monte Carlo simulation is presented which accounts for the lens(es) photon refraction at the sensor interface and angular misalignment between the emitter and receiver. The results show that the ideal divergence of the beam of the emitter is around 15° for a 1 m transmission length, increasing to 22° for a shorter distance of 0.5 m but being independent of the water turbidity. In addition, it is concluded that a seven-lense scheme is approximately three times more tolerant to offset than a single lens. A random forest machine learning algorithm is also assessed for its suitability to estimate the offset and angle of the AUV in relation to the fixed sensor, based on the power distribution of each lens, in real time. The algorithm is able to estimate the offset and angular misalignment with a mean square error of 5 mm (6 mm) and 0.157 rad (0.174 rad) for a distance between the transmitter and receiver of 1 m and 0.5 m, respectively.

This paper proposes a wireless resonant inductive and capacitive (LC) sensor for glucose sensing. A sensor composed of a capacitor with interdigital electrodes and an inductor for glucose sensing is presented. Resonance frequency and impedance were measured as the sensing parameters. A glucose beverage concentration from 0% to 44% is used, resulting in a resonance frequency change from 1.9217 MHz to 1.8681 MHz, and the impedance of the sensor changes from 170.33 Ω to 110.68 Ω. The relationship of both resonance frequency and impedance to glucose beverage concentration is well presented by a decreasing exponential function. Using an exponential regression, the resonance frequency shows an average regression error of 1.38%. Likewise, the impedance shows an average error of 3.47%. The linear range of the sensor is also analyzed in a glucose concentration range between 0% and 4%. The sensor exhibited a sensitivity of 424.6 kHz and 721.6416 Ω, respectively, with a linear regression r2 of 0.9853 and 0.9553, respectively.

A recently developed contactless ultrasonic testing scheme is applied to define the optimal saw-cutting time for concrete pavement. The ultrasonic system is improved using wireless data transfer for field applications, and the signal processing and data analysis are proposed to evaluate the modulus of elasticity of early-age concrete. Numerical simulation of leaky Rayleigh wave in joint-half space including air and concrete is performed to demonstrate the proposed data analysis procedure. The hardware and algorithms developed for the ultrasonic system are experimentally validated with a comparison of saw-cutting procedures. In addition, conventional methods for the characterization of early-age concrete, including pin penetration and maturity methods, are applied. The results demonstrated that the developed wireless system presents identical results to the wired system, and the initiation time of leaky Rayleigh wave possibly represents 5% of raveling damage compared to the optimal saw cutting. Further data analysis implies that saw-cutting would be optimally performed at approximately 11.5 GPa elastic modulus of concrete obtained by the wireless and contactless ultrasonic system.

Agricultural and environmental monitoring programs often require labor-intensive inputs and substantial costs to manually gather data from remote field locations. Recent advances in the Internet of Things enable the construction of wireless sensor systems to automate these remote monitoring efforts. This paper presents the design of a modular system to serve as a research platform for outdoor sensor development and deployment. The advantages of this system include low power consumption (enabling solar charging), the use of commercially available electronic parts for lower-cost and scaled up deployments, and the flexibility to include internal electronics and external sensors, allowing novel applications. In addition to tracking environmental parameters, the modularity of this system brings the capability to measure other non-traditional elements. This capability is demonstrated with two different agri- and aquacultural field applications: tracking moth phenology and monitoring bivalve gaping. Collection of these signals in conjunction with environmental parameters could provide a holistic and context-aware data analysis. Preliminary experiments generated promising results, demonstrating the reliability of the system. Idle power consumption of 27.2 mW and 16.6 mW for the moth- and bivalve-tracking systems, respectively, coupled with 2.5 W solar cells allows for indefinite deployment in remote locations.

This article presents an atypical offline based LoRaWAN application for use in hospital settings, where the ability to maintain network connectivity during internet connection disruption is paramount. A prototype bed rail is demonstrated, providing advanced functionality compared to traditional bed rails. The manufactured prototype provides data to a nurses station reliably and operates under battery backup. The power consumption of the system under different transmission intervals was tested, allowing appropriate battery sizing for different applications to be specified accurately. It is expected that a single LoRaWAN gateway will be able to cover bed rails across an entire modern hospital, allowing minimal infrastructure cost to implement the device or application in a rapidly deployed field hospital.

The development of microelectronics has been achieved by improving its performance through miniaturization. This was possible through the development of silicon-based semiconductor process technology, but recently, the demand for wearable or flexible devices has increased. These devices are made using various functional elements based on materials that are difficult to utilize with semiconductor devices that contain existing hard silicon-based materials and are bent or flexibly stretched. In this study, wireless antennas suitable for wearable devices were implemented in a stretchable form. It was possible to stably receive a wireless signal, even with a strain of 20% or more, and power light-emitting diodes (LEDs), microheaters, etc. By devising a multi-layered stack antenna without the existing semiconductor process, it was possible to improve the antenna’s reception performance. It is expected that this can be applied in various ways to smart wireless sensors and wearable biomedical devices using the near-field communication (NFC) of smartphones.

Wireless electrophysiology opens important possibilities for neuroscience, especially for recording brain activity in more natural contexts, where exploration and interaction are not restricted by the usual tethered devices. The limiting factor is transmission power and, by extension, battery life required for acquiring large amounts of neural electrophysiological data. We present a digital compression algorithm capable of reducing electrophysiological data to less than 65.5% of its original size without distorting the signals, which we tested in vivo in experimental animals. The algorithm is based on a combination of delta compression and Huffman codes with optimizations for neural signals, which allow it to run in small, low-power Field-Programmable Gate Arrays (FPGAs), requiring few hardware resources. With this algorithm, a hardware prototype was created for wireless data transmission using commercially available devices. The power required by the algorithm itself was less than 3 mW, negligible compared to the power saved by reducing the transmission bandwidth requirements. The compression algorithm and its implementation were designed to be device-agnostic. These developments can be used to create a variety of wired and wireless neural electrophysiology acquisition systems with low power and space requirements without the need for complex or expensive specialized hardware.

Thin-film microdevices can be applied to various wearable devices due to their high flexibility compared to conventional bulk-type electronic devices. Among the various microdevice types, many IoT-based sensor devices have been developed recently. In the case of such sensor elements, it is important to control the surrounding environment to optimize the sensing characteristics. Among these environmental factors, temperature often has a great influence. There are cases where temperature significantly affects the sensor characteristics, as is the case for gas sensors. For this purpose, the development of thin-film-type micro-heaters is important. For this study, a wirelessly driven thin-film micro-heater was fabricated on the flexible and stretchable elastomer, a polydimethylsiloxane (PDMS); the antenna was optimized; and the heater was driven at the temperature up to 102 degrees Celsius. The effect of its use on gas-sensing characteristics was compared through the application of the proposed micro-heater to a gas sensor. The heated SnO2 nanowire gas sensor improved the performance of detecting carbon monoxide (CO) by more than 20%, and the recovery time was reduced to less than half. It is expected that thin-film-type micro-heaters that can be operated wirelessly are suitable for application in various wearable devices, including those for smart sensors and health monitoring.

Structural health monitoring (SHM) has been extensively used in the railway industry, with applications ranging from railway infrastructures to carbody shells. An SHM method that dominates monitoring procedures is Acoustic Emissions (AE). The utilisation of the AE method could use a significantly large amount of data, collected and forwarded to terminal computers using wireless communications. Nowadays, the use of 5G is taking over traditional wireless such as Wi-Fi and 4G telecommunications. However, errors in the transmission due to noisy channels may be experienced. The SHM system may result in the wrong detection of a potential defect in a railway infrastructure with dangerous consequences, such as derailment. Hence, methods for adequately dealing with these errors need to be established, such as Forward Error Correction (FEC) codes. In this paper, we suggest the use of the wireless FEC codes applied to a number of deployed AE devices, in order to perform correction at the transmissions. We investigate the new POLAR codes and compare this method with the Reed-Solomon (RS) codes. We present simulations that the POLAR codes are more efficient with trials using the AFF3CT simulator.

Flexible pressure sensors have been widely applied in wearable devices, e-skin, and the new generation of robots. However, most of the current sensors use connecting wires for energy supply and signal transmission, which presents an obstacle for application scenarios requiring long endurance and large movement, especially. Flexible sensors combined with wireless technology is a promising research field for realizing efficient state sensing in an active state. Here, we designed and fabricated a soft wireless passive pressure sensor, with a fully flexible Ecoflex substrate and a multi-walled carbon nanotube/polydimethylsiloxane (MWCNT/PDMS) bilayer pyramid dielectric structure. Based on the principle of the radio-frequency resonator, the device achieved pressure sensing with a changeable capacitance. Subsequently, the effect of the pyramid density was simulated by the finite element method to improve the sensitivity. With one-step embossing and spin-coating methods, the fabricated sensor had an optimized sensitivity of 14.25 MHz/kPa in the low-pressure range. The sensor exhibited the potential for application in limb bending monitoring, thus demonstrating its value for long-term wireless clinical monitoring. Moreover, the radio frequency coupling field can be affected by approaching objects, which provides a possible route for realizing non-contact sensing in applications such as pre-collision warning.

Wireless chemical sensors have been developed as a result of advances in chemical sensing and wireless communication technology. Because of their mobility and widespread availability, smartphones have been extensively combined with sensors such as hand-held detectors, sensor chips, and test strips for biochemical detection. Smartphones are frequently used as controllers, analyzers, and displayers for quick, authentic, and point-of-care monitoring, which may considerably streamline the design and lower the cost of sensing systems. This study looks at the most recent wireless and smartphone-supported chemical sensors. The review is divided into four different topics that emphasize the basic types of wireless smartphone-operated chemical sensors. According to a study of 114 original research publications published during recent years, market opportunities for wireless and smartphone-supported chemical sensor systems include environmental monitoring, healthcare and medicine, food quality, sport, and fitness. The issues and illustrations for each of the primary chemical sensors relevant to many application areas are covered. In terms of performance, the advancement of technologies related to chemical sensors will result in smaller and more lightweight, cost-effective, versatile, and durable devices. Given the limitations, we suggest that wireless and smartphone-supported chemical sensor systems play a significant role in the sensor Internet of Things.

Magnetoelastic sensors, typically made of magnetostrictive and magnetically-soft materials, can be fabricated from commercially available materials into a variety of shapes and sizes for their intended applications. Since these sensors are wirelessly interrogated via magnetic fields, they are good candidates for use in both research and industry, where detection of environmental parameters in closed and controlled systems is necessary. Common applications for these sensors include the investigation of physical, chemical, and biological parameters based on changes in mass loading at the sensor surface which affect the sensor’s behavior at resonance. To improve the performance of these sensors, optimization of sensor geometry, size, and detection conditions are critical to increasing their mass sensitivity and detectible range. This work focuses on investigating how the geometry of the sensor influences its resonance spectrum, including the sensor’s shape, size, and aspect ratio. In addition to these factors, heterogeneity in resonance magnitude was mapped for the sensor surface and the effect of the magnetic bias field strength on the resonance spectrum was investigated. Analysis of the results indicates that the shape of the sensor has a strong influence on the emergent resonant modes. Reducing the size of the sensor decreased the sensor’s magnitude of resonance. The aspect ratio of the sensor, along with the bias field strength, was also observed to affect the magnitude of the signal; over or under biasing and aspect ratio extremes were observed to decrease the magnitude of resonance, indicating that these parameters can be optimized for a given shape and size of magnetoelastic sensor.

Jamming is a malicious radio activity that represents a dreadful threat when employed in critical scenarios. Several techniques have been proposed to detect, locate, and mitigate jamming. Similarly, counter-counter-jamming techniques have been devised. This paper belongs to the latter thread. In particular, we propose a new jammer model: a power-modulated jammer that defies standard localization techniques. We provide several contributions: we first define a new mathematical model for the power-modulated jammer and then propose a throughout analysis of the localization error associated with the proposed power-modulated jammer, and we compare it with a standard power-constant jammer. Our results show that a power-modulated jammer can make the localization process completely ineffective—even under conservative assumptions of the shadowing process associated with the radio channel. Indeed, we prove that a constant-power jammer can be localized with high precision, even when coupled with a strong shadowing effect (σ ≈ 6 dBm). On the contrary, our power-modulated jammer, even in the presence of a very weak shadowing effect (σ < 2 dBm), presents a much wider localization error with respect to the constant-power jammer. In addition to being interesting on its own, we believe that our contribution also paves the way for further research in this area.

Neural interfaces typically focus on one or two sites in the motoneuron system simultaneously due to the limitation of the recording technique, which restricts the scope of observation and discovery of this system. Herein, we built a system with various electrodes capable of recording a large spectrum of electrophysiological signals from the cortex, spinal cord, peripheral nerves, and muscles of freely moving animals. The system integrates adjustable microarrays, floating microarrays, and microwires to a commercial connector and cuff electrode on a wireless transmitter. To illustrate the versatility of the system, we investigated its performance for the behavior of rodents during tethered treadmill walking, untethered wheel running, and open field exploration. The results indicate that the system is stable and applicable for multiple behavior conditions and can provide data to support previously inaccessible research of neural injury, rehabilitation, brain-inspired computing, and fundamental neuroscience.

The effectiveness of inductive power transfer (IPT) presents a serious challenge for improving the global recharge system performance. An electric vehicle (EVs) needs to be charged rapidly and have maximum power when it is charged with wireless technology. Based on various research, the performance of this recharge system is attached to several points and the frequency resonance is one of those parameters that can influence. In this paper, we try to explore the relationship between the obtained power and the signal input frequency for charging a lithium battery, solve the class imbalance problem and understand the maximum allowed frequency. To obtain the results, a mathematical model was first created to demonstrate the relationship, then the dynamic model was validated and tested using the Matlab Simulink platform. The performance of the worldwide wireless recharging system in terms of frequency variation is depicted in a summary graph.

We propose a biomedical sensor system for continuous monitoring of glucose concentration. Despite recent advances in implantable biomedical devices, mm sized devices have yet to be developed due to the power limitation of the device in a tissue. We here present a mm sized wireless system with backscattered frequency-modulation communication that enables a low-power operation to read the glucose level from a fluorescent hydrogel sensor. The configuration of the reader structure is optimized for an efficient wireless power transfer and data communication, miniaturizing the entire implantable device to 3 × 6 mm 2 size. The operation distance between the reader and the implantable device reaches 2 mm with a transmission power of 33 dBm. We demonstrate that the frequency of backscattered signals changes according to the light intensity of the fluorescent glucose sensor. We envision that the present wireless interface can be applied to other fluorescence-based biosensors to make them highly comfortable, biocompatible, and stable within a body.

Radar technology is constantly evolving, and new applications are arising, particularly for the millimeter wave bands. A novel application for radar is gait monitoring for fall prevention, which may play a key role in maintaining the quality of life of people as they age. Alarming statistics indicate that one in three adults aged 65 years or older will experience a fall every year. A review of the sensors used for gait analysis and their applications to technology-based fall prevention interventions was conducted, focusing on wearable devices and radar technology. Knowledge gaps were identified, such as wearable radar development, application specific signal processing and the use of machine learning algorithms for classification and risk assessment. Fall prevention through gait monitoring in the natural environment presents significant opportunities for further research. Wearable radar could be useful for measuring gait parameters and performing fall risk-assessment using statistical methods, and could also be used to monitor obstacles in real-time.

The potential offered by the Internet, combined with the enormous number of connectable devices, offers benefits in many areas of our modern societies, both public and private. The possibility of making heterogeneous devices communicate with each other through the Internet has given rise to a constantly growing scenario, which was unthinkable not long ago. This unstoppable growth takes place thanks to the continuous availability of increasingly sophisticated device features, an ever-increasing bandwidth and reliability of the connections, and the ever-lower consumption of the devices, which grants them long autonomy. This scenario of exponential growth also involves other sectors such as, for example, that of Artificial Intelligence (AI), which offers us increasingly sophisticated approaches that can be synergistically combined with wireless devices and the Internet in order to create powerful applications for everyday life. Precisely for the aforementioned reasons, the community of researchers, year by year, dedicates more time and resources in this direction. It should be observed that this happens in an atypical way concerning the other research fields, and this is because the achieved progress and the developed applications have practical applications in numerous and different domains.

Continuous monitoring of flexions of the trunk via wearable sensors could help various types of workers to reduce risks associated with incorrect postures and movements. Stretchable piezo-capacitive elastomeric sensors based on dielectric elastomers have recently been described as a wearable, lightweight and cost-effective technology to monitor human kinematics. Their stretching causes an increase of capacitance, which can be related to angular movements. Here, we describe a wearable wireless system to detect flexions of the trunk, based on such sensors. In particular, we present: (i) a comparison of different calibration strategies for the capacitive sensors, using either an accelerometer or a gyroscope as an inclinometer; (ii) a comparison of the capacitive sensors’ performance with those of the accelerometer and gyroscope; to that aim, the three types of sensors were evaluated relative to stereophotogrammetry. Compared to the gyroscope, the capacitive sensors showed a higher accuracy. Compared to the accelerometer, their performance was lower when used as quasi-static inclinometers but also higher in case of highly dynamic accelerations. This makes the capacitive sensors attractive as a complementary, rather than alternative, technology to inertial sensors.

The evolution of 5th generation (5G) cellular technology has introduced several enhancements and provides better performance compared to previous generations. To understand the real capabilities, the importance of the empirical studies is significant to also understand the possible limitations. This is very important especially from the service and use case point of view. Several test sites exist around the globe for introducing, testing, and evaluating new features, use cases, and performance in restricted and secure environments alongside the commercial operators. Test sites equipped with the standard technology are the perfect places for performing deep analysis of the latest wireless and cellular technologies in real operating environments. The testing sites provide valuable information with sophisticated quality of service (QoS) indicators when the 5G vertical use cases are evaluated using the actual devices in the carrier grade network. In addition, the Wi-Fi standards are constantly evolving toward higher bit rates and reduced latency, and their usage in 5G dedicated verticals can even improve performance, especially when lower coverage is sufficient. This work presents the detailed comparative measurements between Wi-Fi 6 and 5G New Radio (NR) performance in indoor facilities and extensive results carried out in 5G and beyond test site located in Finland. The results gathered from the extensive test sets indicate that the Wi-Fi 6 can outperform the 5G in the indoor environment in terms of throughput and latency when distance and coverage do not increase enormously. In addition, the usage of wireless technologies allows improved uplink performance, which is usually more limited in cellular networks. The gained results of our measurements provide valuable information for designing, developing, and implementing the requirements for the next-generation wireless applications.

Landslides, often a side effect of mining activities, pose a significant risk to humans and infrastructures such as urban areas, power lines, and dams. Operational ground motion monitoring can help detect the spatial pattern of surface changes and their evolution over time. In this technical note, a commercial, cost-effective method combining a network of geotechnical surface sensors with the InSAR data was reported for the first time to accurately monitor surface displacement. The correlation of both data sets is demonstrated in the Gediminas Castle testbed, where slope failure events were detected. Two specific events were analyzed, and possible causes proposed. The combination of techniques allows one to detect the precursors of the events and characterize the consequences of the failures in different areas in proximity to the castle walls, since the solution allows for the confirmation of long-term drifts and sudden movements in real time. The data from the in situ sensors were also used to refine the satellite data analysis. The results demonstrate that not all events pose a direct threat to the safety of the structure monitored.

A prototype portable device that allows for simultaneous hand and fingers motion and precise force measurements has been. Wireless microelectromechanical systems based on inertial and force sensors are suitable for tracking bodily measurements. In particular, they can be used for hand interaction with computer applications. Our interest is to design a multimodal wireless hand grip device that measures and evaluates this activity for ludic or medical rehabilitation purposes. The accuracy and reliability of the proposed device has been evaluated against two different commercial dynamometers (Takei model 5101 TKK, Constant 14192-709E). We introduce a testing application to provide visual feedback of all device signals. The combination of interaction forces and movements makes it possible to simulate the dynamic characteristics of the handling of a virtual object by fingers and palm in rehabilitation applications or some serious games. The combination of these above mentioned technologies and open and portable software are very useful in the design of applications for assistance and rehabilitation purposes that is the main objective of the device.

This article presents a proposal for a wireless diagnostic system for checking the air tightness of the ventilation network. The solution is designed to increase crew safety in underground mining plants and increase the energy efficiency of the ventube ventilation system. The system is based on sensors measuring the pressure inside the ventilation duct in relation to the barometric pressure in the immediate vicinity of the duct. The flow of diagnostic data is based on a cascade transfer. The data from the first sensor are transferred successively to the last one. Based on the prior calibration of alarm thresholds in each device, the leakage or factor influencing the increase of air flow resistance is located. The article presents the genesis of the creation and discusses the principle and purpose of the system. In the following chapters, the progress of work related to testing the system in laboratory, industrial, and underground conditions at the Velenje Premogovnik mine (Slovenia) is presented. The authors analyze the test results and indicate the directions of possible further work on improving the system. The proposed leak detection system is based on a network of pressure sensors that communicate with each other to clearly pinpoint the leak location. The system has been designed for operation in underground mining plants with limited space.

Older people are very likely to fall, which is a significant threat to the health. However, falls are preventable and are not necessarily an inevitable part of aging. Many different fall detection systems have been developed to help people avoid falling. However, traditional systems based on wearable devices or image recognition-based have many disadvantages, such as user-unfriendly, privacy issues. Recently, WiFi-based fall detection systems try to solve the above problems. However, there is a common problem of reduced accuracy. Since the system is trained at the original signal collecting/training place, however, the application is at a different place. The proposed solution only extracts the features of the changed signal, which is caused by a specific human action. To implement this, we used Channel State Information (CSI) to train Convolutional Neural Networks (CNNs) and further classify the action. We have designed a prototype to test the performance of our proposed method. Our simulation results show an average accuracy of same place and different place is 93.2% and 90.3%, respectively.

Marine life can be severely affected by anthropogenic underwater noise. This latter increased proportionally to the rise of human activities such as maritime traffic, marine civil engineering works, oil- and gas-related activities or offshore wind farms; so much so that, currently, it can be considered a threat to the environment. Assessing underwater noise requires quite some investments both in personnel and instrumentation. If this is affordable by several governmental and scientific organizations, this cannot be extended straightforwardly to all research initiatives or to developing countries. In addition, time and geographic coverage of monitoring can also be significantly limited by the costs of multiple installations. We explore the possibility to use a solution based on off-the-shelf and low-cost technologies combined with a scalable infrastructure developed with open-source tools only. The perspective to avoid proprietary solutions allows great flexibility in extending the current paradigm toward real-time transmission, processing, and web-based data access. Our solution has been deployed at sea in November 2020 and is providing data continuously ever since. First results from the analysis of these data allowed us to highlight several interesting abiotic and anthropogenic temporal patterns.

A new type of wireless passive, high sensitivity, high temperature sensor was designed to meet the real-time temperature test in the harsh aero-engine environment. The sensor consists of a complementary split ring resonator and a substrate integrated circular waveguide (CSRR-SICW) structure and is based on high temperature resistant Si3N4 ceramic as the substrate material. Temperature is measured by real-time monitoring of the resonant frequency of the sensor. In addition, the ambient temperature affects the dielectric constant of the dielectric substrate, and the resonant frequency of the sensor is determined by the dielectric constant, so the function relationship between temperature and resonant frequency can be established. The experimental results show that the resonant frequency of the sensor decreases from 11.3392 GHz to 11.0648 GHz in the range of 50–1000 °C. The sensitivity is 123 kHz/°C and 417 kHz/°C at 50–450 °C and 450–1000 °C, respectively, and the average test sensitivity is 289 kHz/°C. Compared with previously reported high temperature sensors, the average test sensitivity is approximately doubled, and the test sensitivity at 450–1000 °C is approximately three times higher. Therefore, the proposed high sensitivity sensor has promising prospects for high temperature measurement.